Initial Questions to Explore

Setup

Load required libraries for wrangling data, charting, and mapping

library(plyr,quietly = T) # data wrangling
library(dplyr,quietly = T) # data wrangling
library(ggplot2, quietly = T) # charting
library(ggthemes,quietly = T) # so I can add the highcharts theme and palette
library(scales,quietly = T) # to load the percent function when labeling plots

Set our preferred charting theme

theme_set(theme_minimal()+theme_hc()+theme(legend.key.width = unit(1.5, "cm")))

Run script to get elk population data

source('~/_code/colorado-dow/datasets/read colorado dow population estimates.R', echo=F)

Table of the elk herd data

COElkPopulationAll
source('~/_code/colorado-dow/datasets/Colorado GMUnit and Road data.R', echo=F)
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/psarnow/_code/colorado-dow/datasets/CPW_GMUBoundaries/BigGameGMUBoundaries03172015.shp", layer: "BigGameGMUBoundaries03172015"
## with 185 features
## It has 12 fields
## Integer64 fields read as strings:  GMUID 
## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/psarnow/_code/colorado-dow/datasets/ne_10m_roads/ne_10m_roads.shp", layer: "ne_10m_roads"
## with 56601 features
## It has 29 fields
## Integer64 fields read as strings:  scalerank question

Take a peak at the boundary data

head(Unitboundaries2)

Elk Population

Statewide

Population of the Data Analysis Units (DAU) – Herd IDs

# Group DAUs
COElkPopulationStatewide <- summarise(group_by(COElkPopulationAll,Year,DAU),
                                      Population.DAU = mean(Population.DAU))
# Group Years
COElkPopulationStatewide <- summarise(group_by(COElkPopulationStatewide,Year),
                                      Population = sum(Population.DAU))

ggplot(COElkPopulationStatewide, aes(Year,Population)) +
  geom_bar(stat="identity",fill=ggthemes_data$hc$palettes$default[2]) +
  coord_cartesian(ylim = c(250000,300000)) +
  labs(title="Statewide Elk Population", caption="source: cpw.state.co.us")

In recent years it appears the state’s elk population has stabalized and is close to the 10 year median.

Population by Unit

I’d like to know where the elk are distributed across the state. I could look at each year but in this case I am only interested in the most recent year with population data.

# Group Units
COElkUnitPopulation <- summarise(group_by(COElkPopulationAll,Year,Unit),
                                 Population = mean(Population.Unit))

Year2016 <- filter(COElkUnitPopulation, Year == "2016")
PopulationtoPlot <- left_join(Unitboundaries2,Year2016, by=c("Unit"))
ggplot(PopulationtoPlot, aes(long, lat, group = group)) + 
  geom_polygon(aes(fill = Population),colour = "grey50", size = .2) + #Unit boundaries
  geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
  geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=3) + #Unit labels
  scale_fill_distiller(palette = "Greens",direction = 1,na.value = 'light grey') +
  xlab("") + ylab("") +
  labs(title="2016 Colorado Elk Population", caption="source: cpw.state.co.us")

TODO - commentary

Alternative mapping option

library(ggmap)
theme_set(theme_classic())
colorado_stamen_map <- qmap(location=c(lon=-105.54,lat=39.1438), zoom=7, source = "stamen",maptype = "toner")   
colorado_stamen_map +
  geom_polygon(data = PopulationtoPlot, aes(long,lat,group=group,fill = Population),colour = "grey50", size = .2,alpha=.5) + #Unit boundaries
  geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=2) + #Unit labels
  scale_fill_distiller(palette = "Greens",direction = 1,na.value = 'grey') +
  xlab("") + ylab("") +
  theme(panel.background = element_rect(fill='white')) +
  theme(panel.grid.major= element_blank()) +
  theme(panel.grid.minor= element_blank()) +
  labs(title="2016 Colorado Elk Population", caption="source: cpw.state.co.us")

I considered using some real maps like this one, but they get a little busy with more labeling.

Back to our theme

theme_set(theme_minimal()+theme_hc()+theme(legend.key.width = unit(1.5, "cm")))

Population Rank of the Units

Would also be beneficial to rank each unit so I can reference later. In this case average the population of the last few years

PopulationRanklast3 <- filter(COElkUnitPopulation, as.numeric(Year) >= 2014)
PopulationRanklast3 <- summarise(group_by(PopulationRanklast3,Unit),
                                 Population = mean(Population,na.rm = T))
PopulationRanklast3$PopulationRank = rank(-PopulationRanklast3$Population)

PopulationRanklast3 <- filter(PopulationRanklast3, PopulationRank <= 50) # top 50 units

In order for the chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor.

PopulationRanklast3 <- PopulationRanklast3[order(-PopulationRanklast3$Population), ]  # sort
PopulationRanklast3$Unit <- factor(PopulationRanklast3$Unit, levels = PopulationRanklast3$Unit)  # to retain the order in plot.

Lollipop Chart Conveys the same information as in bar charts. By reducing the thick bars into thin lines, it reduces the clutter and lays more emphasis on the value. It looks nice and modern.

ggplot(PopulationRanklast3, aes(x=Unit, y=Population)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=Unit, 
                   xend=Unit, 
                   y=0, 
                   yend=Population)) + 
  labs(title="Average Elk Population 2014-2016\nTop 50 Units", subtitle="Population by Unit", caption="source: cpw.state.co.us")

TODO - commentary

Conclusion

There are sizable herds in Southern Colorado (80-82), as well as Southwestern Colorado. The Northwestern herd is large too. The number of elk are less along the Front Range.

After looking at this data, I’m wondering how many hunters are in each of these areas. I would expect that CPW associates the number of hunters to how many elk are in each unit.